More Personalised Learning Informed by Better Data

In a recent post on a Report on Modernisation of Higher Education I described how the High Level Group’s report on the Modernisation of Higher Education which covers New modes of learning and teaching in higher education gave a high profile to the importance of learning analytics. The report includes a section entitled More personalised learning informed by better data which explains how:

In traditional lecture hall settings, it is difficult for a teacher to follow the progress of each and every student. It is impossible to adapt the pace of the course to match individual needs. Online provision allows the capturing of a range of data that can be used to monitor student progress. Advances in big data and learning analytics can help our higher education system customise teaching tools and develop more personalised learning pathways based on student data. However, the collection, analysis and use of learning data must only occur with the explicit consent of the student.

Data can capture how students engage in the course, interact with other students and retain concepts over time. It can provide information on the learning process as opposed to just learning outcomes. Teachers can experiment with different approaches and examine the immediate impact. Data can also be used to identify at-risk students at an early stage, assisting in efforts to increase retention rates. While still a relatively young field, exciting developments in learning analytics are underway. Several universities in the United States have programmed automatic dashboards, giving teachers the possibility to monitor their student’s performance live. The massive availability and usability of data has also great potential for empirical research on learning and teaching. Stanford’s Lytics Lab is one example that applies empirical research to better understand the performance of students. Learning process and feedback tools are yet another development that allows students to monitor their own performance and adapt it accordingly. The Open-Learning Initiative of the Carnegie Melon University and the Check-My-Activity-Tool of the University of Maryland are two examples of these canadian online pharmacy no rx promising developments.

Note that the 37 page long report is available in PDF format.

Open Learning Analytics Network Summit

Open learning analytics network summit 2014As part of its work on the LACE (Learning Analytics Community Exchange) project, Cetis is organising a one day summit event to broker collaboration around the idea of an Open Learning Analytics platform – based on principles of modularity, open architectures, and open standards – in Amsterdam on 1st December 2014, collaborating with colleagues from the University of Amsterdam and the Apereo Learning Analytics Initiative.

We have seen how the Modernisation of Higher Education report which covers New modes of learning and teaching in higher education has given a high profile to the importance of learning analytics.

In addition Learning Analytics and interoperability are identified as key areas for research and innovation in the Horizon 2020 call ICT-20, “Technologies for better human learning and teaching“.

The time is therefore right to gather a European critical mass of activity behind the idea of an Open Learning Analytics platform.

The purpose of the Open Learning Analytics Network Summit Europe event is to develop a shared European perspective on the concept of an Open Learning Analytics framework, based on a critical view of where we are now and what is feasible in the next 3-5 years.

The intended outcomes of the summit are concrete plans for collaborative research and innovation. These plans will set out to meet the challenges and realise the possibilities of 21st century learning and teaching, including those outlined in the ICT-20 call, in a way that properly embeds the Open Learning Analytics principles.

The Open Learning Analytics Network – Summit Europe 2014 will take place at the Allard Pierson Museum, Amsterdam on 1st December 2014. Participation is invited from public and private sectors, including innovators from all sectors of education and training, open source and proprietary software developers, people with experience of learning analytics interoperability and architects of modular and distributed systems.

Further information including the full call for participation is available on the LACE project web site.


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